Relatively few retailers include metrics such as product returns in their customer selection and optimal resource allocation algorithms when measuring and maximizing customer value. Even when they do ...include this metric, increases in product return behavior are usually considered merely an economic cost that must be managed by decreasing the marketing resource allocations toward the customers making the returns. However, recent research has suggested that satisfactory product return experiences can actually benefit firms by lowering the customer's perceived risk of current and future purchases. To better understand the role of this perceived risk in the firm-customer exchange process, the authors conduct a large-scale customer selection and optimal resource allocation field experiment with 26,000 customers from an online retailer over six months. They find that the firm is able to increase both its shortand long-term profits when accounting for the perceived risk related to product returns in addition to managing product return costs. Furthermore, the authors find that by including this risk, rather than simply implementing traditional customer lifetime value-based models generically, the firm can target more profitable customers.
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. ...After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure
. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold
, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.
Customer behavior across competitive loyalty programs Khodakarami, Farnoosh; Andrew Petersen, J.; Venkatesan, Rajkumar
Journal of the Academy of Marketing Science,
05/2024, Letnik:
52, Številka:
3
Journal Article
Recenzirano
Customers can belong to multiple competing loyalty programs each with multiple reward levels. We extend loyalty program theories by proposing five mechanisms that capture the competitive effects in ...multi-firm, multi-level loyalty programs. We empirically test our hypotheses using data from a loyalty program management app where customers manage points independently across competing firms. We utilize goal shielding theory to show how a customer’s purchase at the focal firm is affected by the customer’s purchases and redemptions across competing firms. Specifically, we find that a customer’s purchase probability at the focal firm decreases after they qualify for a reward independent of redemption at a competing firm (competitive mere reward qualification) and after they redeem a reward at a competing firm (competitive rewarded behavior). Further, we find that the customer’s purchase probability at the focal firm increases if the customer is far from both the qualified and higher-level rewards at the competing firm (competitive stuck-in-the-middle), and if the customer accelerated their purchase frequency to qualify for or redeem a reward at the competing firm (competitive effort balancing post qualification and redemption). Four lab experiments supplement our empirical findings with causal evidence. Our research shows that customer progress toward a goal in a loyalty program is influenced by competing loyalty programs.
The firm-customer exchange process consists of three key parts: (1) firm-initiated marketing communications, (2) customer buying behavior, and (3) customer product return behavior. To date, the ...literature in marketing has largely focused on how marketing communications affect customer buying behavior and, to some extent, how past buying behavior affects a firm's decisions to initiate future marketing communications. However, the literature on product returns is sparse, especially in relation to analyzing individual customer product return behavior. Although the magnitude of the value of product returns is known to be high ($100 billion per year), how it affects customer buying behavior is not known because of a lack of data availability and understanding of the role of product returns in the firm-customer exchange process. Given that product returns are considered a hassle for a firm's supply chain management and a drain on overall profitability, it is important to study product return behavior. Thus, the authors empirically demonstrate the role of product returns in the exchange process by determining the exchange process factors that help explain product return behavior and the consequences of product returns on future customer and firm behavior. In addition, the authors demonstrate that product returns are inevitable but by no means evil. PUBLICATION ABSTRACT
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•Understanding the drivers of organic clicks for search engine optimization (SEO).•Develop a model that provides guidance for SEO practitioners on keyword selection.•Online authority ...is important at driving organic clicks for informational searches.•Content relevance is important at driving organic clicks for transactional searches.
We build an empirical framework using search queries and organic click data which provides model-based guidance to SEO practitioners for keyword selection and web content creation. Specifically, we study how search characteristics (search query popularity, search query competition, search query specificity, and search intent) and website characteristics (content relevance and online authority) interact to affect the expected organic clicks as well as the organic rank a website receives from the search engine result page (SERP). It is often thought that content relevance is a key factor to improve the effectiveness of SEO. We find, however, that content relevance is an important factor in driving organic clicks only when the consumer is farther along in the customer journey and searching for ways to purchase a product. Whereas, when the customer is at the awareness stage and looking for product information, online authority is the key driver of organic clicks.
A growing body of evidence in humans implicates chronic activation of the innate immune response in the brain as a major cause of neuropathology in various neurodegenerative conditions, although the ...mechanisms remain unclear. In an unbiased genetic screen for mutants exhibiting neurodegeneration in Drosophila , we have recovered a mutation of dnr1 (defense repressor 1), a negative regulator of the Imd (immune deficiency) innate immune-response pathway. dnr1 mutants exhibit shortened lifespan and progressive, age-dependent neuropathology associated with activation of the Imd pathway and elevated expression of AMP (antimicrobial peptide) genes. To test the hypothesis that overactivation of innate immune-response pathways in the brain is responsible for neurodegeneration, we demonstrated that direct bacterial infection in the brain of wild-type flies also triggers neurodegeneration. In both cases, neurodegeneration is dependent on the NF-κB transcription factor, Relish. Moreover, we found that neural overexpression of individual AMP genes is sufficient to cause neurodegeneration. These results provide a mechanistic link between innate immune responses and neurodegeneration and may have important implications for the role of neuroinflammation in human neurodegenerative diseases as well.
Precise temporal control of gene expression or deletion is critical for elucidating gene function in biological systems. However, the establishment of human pluripotent stem cell (hPSC) lines with ...inducible gene knockout (iKO) remains challenging. We explored building iKO hPSC lines by combining CRISPR/Cas9-mediated genome editing with the Flp/FRT and Cre/LoxP system. We found that “dual-sgRNA targeting” is essential for biallelic knockin of FRT sequences to flank the exon. We further developed a strategy to simultaneously insert an activity-controllable recombinase-expressing cassette and remove the drug-resistance gene, thus speeding up the generation of iKO hPSC lines. This two-step strategy was used to establish human embryonic stem cell (hESC) and induced pluripotent stem cell (iPSC) lines with iKO of SOX2, PAX6, OTX2, and AGO2, genes that exhibit diverse structural layout and temporal expression patterns. The availability of iKO hPSC lines will substantially transform the way we examine gene function in human cells.
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•Efficient strategy outlined for engineering clonal inducible gene knockout hPSC lines•Dual-sgRNA targeting is essential for precise biallelic knockin of FRT•Inducible gene knockout can occur in all cells at any differentiation stages•Multiple genes can be targeted for inducible knockout
By combining CRISPR/Cas9-mediated genome editing with the Flp/FRT and Cre/LoxP system, Chen et al. developed an efficient two-step strategy to generate inducible gene knockout hPSC lines with predictable gene mutations upon tamoxifen treatment at any stages of differentiation. The iKO hPSC lines will enable the elucidation of gene functions throughout differentiation.
Applying and improving AlphaFold at CASP14 Jumper, John; Evans, Richard; Pritzel, Alexander ...
Proteins, structure, function, and bioinformatics,
December 2021, Letnik:
89, Številka:
12
Journal Article
Recenzirano
Odprti dostop
We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein Structure Prediction ...(CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end‐to‐end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modeling targets, and represents a significant improvement in the state of the art in protein structure prediction. We reported how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large‐scale structure prediction.
Domo Arigato Mr. Roboto van Doorn, Jenny; Mende, Martin; Noble, Stephanie M. ...
Journal of service research : JSR,
02/2017, Letnik:
20, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Technology is rapidly changing the nature of service, customers’ service frontline experiences, and customers’ relationships with service providers. Based on the prediction that in the marketplace of ...2025, technology (e.g., service-providing humanoid robots) will be melded into numerous service experiences, this article spotlights technology’s ability to engage customers on a social level as a critical advancement of technology infusions. Specifically, it introduces the novel concept of automated social presence (ASP; i.e., the extent to which technology makes customers feel the presence of another social entity) to the services literature. The authors develop a typology that highlights different combinations of automated and human social presence in organizational frontlines and indicates literature gaps, thereby emphasizing avenues for future research. Moreover, the article presents a conceptual framework that focuses on (a) how the relationship between ASP and several key service and customer outcomes is mediated by social cognition and perceptions of psychological ownership as well as (b) three customer-related factors that moderate the relationship between ASP and social cognition and psychological ownership (i.e., a customer’s relationship orientation, tendency to anthropomorphize, and technology readiness). Finally, propositions are presented that can be a catalyst for future work to enhance the understanding of how technology infusion, particularly service robots, influences customers’ frontline experiences in the future.
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort
, the structures of around ...100,000 unique proteins have been determined
, but this represents a small fraction of the billions of known protein sequences
. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem'
-has been an important open research problem for more than 50 years
. Despite recent progress
, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)
, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
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